Monitoring Requirements Research Articles

Application of Intelligent Sensor in Mining Electrical Equipment Collection

In order to meet the informatization requirements of coal mine safety monitoring, the author proposes a method for the application of smart sensors in the acquisition of mine electrical equipment. The system uses a variety of sensor fusion methods, with the help of Zigbee wireless network nodes, and passes the data collected by the sensor to the MCU core processor; thus, the collected data are processed, and then, the RS-485 communication protocol is used to upload the data to the upper station; finally, the monitoring of coal mine safety is realized through the background monitoring interface. Experimental results show that, among the five randomly selected nodes, most of the errors between the actual measured results and the collected results are concentrated within the 2% error range. Conclusion. The effect of the abovementioned acquisition scheme in coal mine application is verified, so as to realize the scientific monitoring of coal mine safety.

Data Model Design to Support Data-Driven IT Governance Implementation

Organizations must quickly adapt their processes to understand the dynamic nature of modern business environments. As highlighted in the literature, centralized governance supports decision-making and performance measurement processes in technology companies. For this reason, a reliable decision-making system with an integrated data model that enables the rapid collection and transformation of data stored in heterogeneous and different sources is needed. Therefore, this paper proposes the design of a data model to implement data-driven governance through a literature review of adopted approaches. The lack of a standardized procedure and a disconnection between theoretical frameworks and practical application has emerged. This paper documented the suggested approach following these steps: (i) mapping of monitoring requirements to the data structure, (ii) documentation of ER diagram design, and (iii) reporting dashboards used for monitoring and reporting. The paper helped fill the gaps highlighted in the literature by supporting the design and development of a DWH data model coupled with a BI system. The application prototype shows benefits for top management, particularly those responsible for governance and operations, especially for risk monitoring, audit compliance, communication, knowledge sharing on strategic areas of the company, and identification and implementation of performance improvements and optimizations.

Spatial downscaling of surface ozone concentration calculation from remotely sensed data based on mutual information

Accurate near surface ozone concentration calculation with high spatial resolution data is very important to solve the problem of serious ozone pollution and health impact assessment. However, the existing remotely sensed ozone products cannot meet the requirements of high spatial resolution monitoring. In this study, surface O3 concentration (at 30 km spatial resolution) was extracted from the daily TROPOMI O3 profile products. Meanwhile, this study improved the downscaling algorithm based on the mutual information and applied it to the mapping of surface O3 concentration in China. Combined with the surface O3 concentration data (with 5 km spatial resolution) obtained by using the Light Gradient Boosting Machine (LightGBM) algorithm and AOD data (at 1 km resolution) from MODIS, the downscaling of TROPOMI ground O3 concentration data from 30 km to 1 km has been achieved in this study. The downscaled ground O3 concentration data were subsequently validated using an independent ground O3 concentration dataset. The main conclusion of this study is that the mutual information entropy between the bottom layer data of the TROPOMI ozone profile (at 30 km resolution), LightGBM surface O3 concentration data (at 5 km resolution), and MCD19A2 AOD data (at 1 km resolution) can accurately reduce the spatial resolution of ozone concentration in the ground layer. The downscaling procedure not only resulted in increase of the spatial resolution over the whole area but also significant improvements in precision with coefficient of determination (R2) increased from 0.733 to 0.823, mean biased error decreased from 7.905 μg/m3 to 3.887 μg/m3, and root-mean-square error decreased from 14.395 μg/m3 to 8.920 μg/m3 for ground O3 concentration.

Gold island‐enhanced multiplex quantum dots fluorescent system for biomedical analysis of circulating tumor nucleic acids

AbstractCirculating tumor nucleic acids (CTNAs) have been employed as the potential markers for tumor diagnosis and management, which are highly correlated with the tumorigenesis, progression, and metastasis processes. Therefore, it is important to develop a highly‐sensitive and reliable method for the detection of CTNAs, especially the multiplex point mutation detection of blood‐derived CTNAs. Herein, a versatile gold island‐enhanced multiplex quantum dots (GIEM‐QDs) fluorescent platform was constructed for the highly‐sensitive detection of CTNAs in serum. The GIEM fluorescent strategy was designed as the highly‐efficient signal giving‐out mode which could amplify the fluorescence intensity of QDs, thus realizing a homogeneous platform for the enrichment, multiplex detection, and point mutations monitoring of CTNAs with the principle of base‐stacking. A high sensitivity of 10 amol and desirable specificity was achieved, and the performance index for analysis of clinical tumor patients’ samples indicated that the GIEM‐QDs fluorescent strategy could realize multiplex point mutation detection of CTNAs in complex blood samples. Hence, this platform could achieve a high detection rate in clinical samples that suitably met the clinical requirements for multiplex detection and point mutations monitoring of CTNAs, and thus has the potential to serve as the tumor liquid biopsy strategy based on CTNAs.

Performance Assessment for a Guided Wave-Based SHM System Applied to a Stiffened Composite Structure.

To assess the ability of structural health monitoring (SHM) systems, a variety of prerequisites and contributing factors have to be taken into account. Within this publication, this variety is analyzed for actively introduced guided wave-based SHM systems. For these systems, it is not possible to analyze their performance without taking into account their structure and their applied system parameters. Therefore, interdependencies of performance assessment are displayed in an SHM pyramid based on the structure and its monitoring requirements. Factors influencing the quality, capability and reliability of the monitoring system are given and put into relation with state-of-the-art performance analysis in a non-destructive evaluation. While some aspects are similar and can be treated in similar ways, others, such as location, environmental condition and structural dependency, demand novel solutions. Using an open-access data set from the Open Guided Waves platform, a detailed method description and analysis of path-based performance assessment is presented.The adopted approach clearly begs the question about the decision framework, as the threshold affects the reliability of the system. In addition, the findings show the effect of the propagation path according to the damage position. Indeed, the distance of damage directly affects the system performance. Otherwise, the propagation direction does not alter the potentiality of the detection approach despite the anisotropy of composites. Nonetheless, the finite waveguide makes it necessary to look at the whole paths, as singular phenomena associated with the reflections may appear. Numerical investigation helps to clarify the centrality of wave mechanics and the necessity to take sensor position into account as an influencing factor. Starting from the findings achieved, all the issues are discussed, and potential future steps are outlined.

PAHs and trace metals in marine surficial sediments from the Porcupine Bank (NE Atlantic): A contribution to establishing background concentrations

Little is known about pollutant concentrations in marine remote areas such as the Porcupine Bank in the NE Atlantic Ocean. Remote locations are much less studied than the more readily accessible coastal areas, nevertheless, are of great importance both to unveil how far human influence has reached and, are more challenging, to infer background concentrations (BCs) of naturally occurring and/or anthropogenic pollutants. Knowledge of contaminant background levels are critical for establishing remediation and management strategies; in addition, background assessment is heavily emphasised in legislative monitoring requirements. Obtaining suitable sampling locations to assess background concentrations can be challenging, as samples should match characteristics to the target area and not be impacted by historical or current inputs of the chemical substances of study. Anthropogenic impacts generally deem local-based sampling to be unsuitable to infer background pollution values. Sampling in remote areas such as the Porcupine Bank better fulfils low impact and pressure requirements making them more suitable for the derivation of background concentration estimates for organic compounds and metals.The total concentrations of polycyclic aromatic hydrocarbons (PAHs) and metal(loid)s in deep sea sediments were evaluated and both an environmental status and a set of concentration data were adequate to derive BCs concentrations is presented. Concentration data indicated, in comparison with previous published data, trace level presence of PAHs and metal(loids) in sediments from the Porcupine Bank. These values will provide a valuable tool to identify the natural presence of organic and inorganic compounds and be the basis to perform a sound environmental assessment.

IntOpt: In-band Network Telemetry optimization framework to monitor network slices using P4

The emergence of Network Functions Virtualization (NFV) is being heralded as an enabler of the recent technologies such as 5G/6G, IoT and heterogeneous networks. Existing NFV monitoring frameworks either do not have the capabilities to express the range of telemetry items needed to perform management or do not scale to large traffic volumes and rates. We present IntOpt, a scalable and expressive telemetry system designed for flexible NFV monitoring using active probing and P4. IntOpt allows us to specify monitoring requirements for individual service chain, which are mapped to telemetry item collection jobs that fetch the required telemetry items from P4 programmable data-plane elements. We propose mixed integer linear program (MILP) as well as a simulated annealing based random greedy (SARG) meta-heuristic approach to minimize the overhead due to active probing and collection of telemetry items. Using P4-FPGA, we benchmark the overhead for telemetry collection. Our numerical evaluation shows that the proposed approach can reduce monitoring overheads by 39% and monitoring delays by 57%. Such optimization may as well enable existing expressive monitoring frameworks to scale for larger real-time networks.

Awareness of Biomedical Waste Management among Health Care Personnel in Bareilly International University, Bareilly, India

INTRODUCTION: Biomedical Waste is defined as any waste, which is generated during the diagnosis, treatment or immunisation of human beings or animals or in research activities pertaining thereto or in the production or testing of biological products. Healthcare workers are one of the main contributors to the healthcare waste management process. AIM: The study aimed to assess the level of awareness and attitude regarding biomedical waste management policy and practices among health care workers at Bareilly International University, Bareilly, India. MATERIALS AND METHOD: A cross-sectional study was conducted using a questionnaire with closed-ended questions which was distributed to 150 medical, 154 dental and 100 Class IV employees at Bareilly International University, India. The responses in the questionnaire were used to assess their knowledge of biomedical waste disposal. The responses were evaluated, and each participant's percentage of correct and incorrect responses for each question was calculated. RESULTS: The results indicated that all healthcare professionals had a low degree of knowledge and awareness regarding the risks associated with biomedical waste formation, legal requirements, and management. Only 11 (7.14%) dental interns had excellent knowledge about biomedical waste generation and legislation while 5 (3.33%) medical interns had extremely poor knowledge about it. Only 4 (4%) Class IV employees had an excellent awareness of biomedical waste management practices. Only 34 (22.08%) dental interns had an excellent level of knowledge of needle-stick injuries among health care personnel. CONCLUSION: It was concluded that health care professionals have a low degree of knowledge and awareness regarding the risks of biomedical waste generation, laws, and management at Bareilly International University, India hence there is a requirement for regular monitoring and training at all levels of healthcare.

Nucleic Acid Quantification with Amplicon Yield in Recombinase Polymerase Amplification.

Amplification-based quantitative polymerase chain reaction (qPCR) provides accurate and sensitive nucleic acid quantification. However, the requirement of temperature cycling and real-time monitoring limits its translation to many settings. Quantitative isothermal amplification methods alleviate the need for thermal cyclers; however, they still require continuous monitoring of the nucleic acid amplification on sophisticated readers. Here, we adapted an isothermal recombinase polymerase amplification (RPA) reaction to develop a semiquantitative method that relies on the final amplicon yield to estimate the initial target nucleic acid copy number. To achieve this, we developed a phenomenological model that captures the essential RPA dynamics. We identified reaction conditions that constrained the reaction yield corresponding to the starting DNA template concentration. We validated these predictions experimentally and showed that the amplicon yields at the end of the RPA reaction correlated well with the starting DNA concentration while reducing nonspecific amplification robustly. We demonstrate this approach, termed quantitative endpoint RPA (qeRPA), to detect DNA over five log orders with a detection limit of 100 molecules. Using a linear regression model of the normalized endpoint intensity (NEI) standard curve, we estimate the viral load from the serum of dengue virus-infected patients with comparable performance to qPCR. Unlike the conventional isothermal quantitative methods, qeRPA can be employed for robust and sensitive nucleic acid estimation at close to room temperature without real-time monitoring and can be beneficial for field deployment in resource-limited settings.

Displacement Monitoring Requirements and Laser Displacement Monitoring Technology of Bridges with Short and Medium Spans

Short and medium-span bridges account for more than 90% of the bridges in China, and it is not suitable to use conventional health monitoring technology to monitor them. Based on an analysis of the proportion of live load, displacement under load, the action time of live load and other factors affecting short and medium-span bridges, this paper determines a feasible technical solution using mid-span deflection to monitor bridges with requirements including measuring range, resolution and accuracy. A set of wireless laser displacement monitoring technologies and equipment is studied by using the phase laser ranging method and the principle of pulsed laser ranging, and the reliability of the data is verified by experimental tests. Using both practical application cases and economic analysis, this paper proves that the technology has significant application advantages for short and medium-span bridges.

Visualized Stacked Denoising Auto-Encoder Model for Extracting and Evaluating the State Features of Rolling Bearings

Extracting intuitive operating state features from vibration signals without prior knowledge is a prospective requirement for health monitoring and fault diagnosis in bearings. In this paper, a visualized stacked denoising auto-encoder (VSDAE) model is proposed for the unsupervised extraction and quantitative evaluation of bearings’ state features. First, the stacked denoising auto-encoder (SDAE) was used to reconstruct vibration signals. The intermediate vector of the SDAE, which is a high-information-density representation of vibration signals, was regarded as the pending state feature. Then, the dimension of the intermediate vector was reduced by the t-distributed stochastic neighbor embedding (t-SNE) method to the two-dimensional visualization space. Finally, the silhouette coefficient of feature distribution was calculated to quantitatively evaluate the extracted features. The proposed model was evaluated using experimental bearing signals simulating various operating states. The results proved that the features, extracted and evaluated by the VSDAE, allowed the recognition of the operating states of the examined bearings.

Environmental Monitoring and Restoration of Ecological Cycle Index System Based on IoT Agriculture

Environmental factors have a direct impact on the development of agriculture, so it is particularly important to detect the environment of the agroecological cycle index system. Although there are some plans for environmental monitoring, most of them are environmental monitoring for a larger concept, but this study is specific to the actual object. This article takes farmland as the research object and comprehensively uses embedded technology, information monitoring technology, and network technology based on the analysis of the research status and system application of the farmland environmental monitoring system. This article studies and designs a real-time online remote monitoring system for farmland ecological environment based on embedded architecture and GPRS technology. By using sensors to obtain farmland information, it is displayed on the monitoring center and mobile client, and data information is obtained in real time. It manages and protects the farmland environment in advance. This article measures and analyzes the data from the final experiment. The experimental results show that the monitoring system can accurately collect farmland data in real time. And through the embedded server and the Internet, the data can be remotely transmitted in real time and displayed on the monitoring center and mobile client software. The results showed that PM2.5 was 30 μg/m3 at 20:00. The experimental data have the characteristics of real time and stability, which can meet the requirements of real-time network remote monitoring and transmission of data.

Analysis and simulation of typical faults of electro-hydraulic proportional servo valve

In this paper, the simulation of three typical faults of electro-hydraulic proportional servo valve is presented, and the physical model of servo valve and its faults is constructed by AMESIM software. Electro-hydraulic proportional servo valve has high steady-state accuracy, excellent dynamic performance, stability and reliability, and has been gradually applied in the control field. Therefore, higher requirements are put forward for real-time state monitoring and typical fault research of electro-hydraulic proportional servo valve. In this paper, aiming at the requirement of real-time monitoring of the servo valve, the mathematical model of three typical faults, such as stuck, spool wear and zero deviation, is sought, and the feasibility of the model is verified through simulation.

Cutting tool wear monitoring in milling processes by integrating deep residual convolution network and gated recurrent unit with an attention mechanism

The conditions of cutting tool wear in the machining process have a great influence on the dimension accuracy and surface integrity of a workpiece. Real-time tool condition monitoring can alert a worker to changes in a tool in time before tool failure occurs to reduce downtime and improve product quality. However, traditional monitoring methods are no longer able to meet the requirements of real-time monitoring in current intelligent manufacturing. Therefore, an online monitoring method based on deep learning to monitor the cutting tool conditions is proposed In this research. In this monitoring model, one-dimensional wide-kernel convolution is used to filter the high-frequency noise in the multi-dimensional raw signals, and then a deep residual convolution layer is adopted to adaptively extract the multi-dimensional spatial features of the signals, and a gated recurrent unit (GRU) layer with an attention mechanism is employed to adaptively extract the hidden temporal features of the sequence data and adaptively assign the corresponding weights, and finally, a regression layer is used to predict the wear value of the tool according to the extracted features. The PHM 2010 challenge dataset is used to illustrate and evaluate this proposed method. The experiment results show that the proposed method can accurately predict the future flank wear values in real time based on the acquired multi-dimensional signals and avoid the complexity of the manual feature extraction.

Lifetime-Maximized Strong Barrier Coverage of 3D Camera Sensor Networks

Camera sensor networks (CSNs) have advantages on providing the precise and multimedia information for plenty of applications. The high coverage quality of CSNs especially satisfies the monitoring requirements of barrier coverage. In three-dimensional (3D) application scenarios, the tracking of the potential intruder in the monitored irregular spaces brings more difficulties and challenges on strong barrier coverage for CSNs. In this paper, we consider the strong barrier coverage problem in 3D CSNs and focus on the objective of monitoring the intruder with high resolution and maximizing the network lifetime. We firstly introduce the definition and hardness proof for the problem based on the irregular space model and the network model, which adopts the Region of Interest (ROI) sensing model with high effective resolution. Secondly, we design two sleep-and-awake scheduling algorithms for the problem in homogeneous and heterogeneous networks, respectively, which are based on the auxiliary graph transformation and the disjoint flows construction. To evaluate these algorithms’ performance on the lifetime maximization, we conduct extensive simulation experiments and analyze their results on their advantages and applicable scenarios.

Performance of Storm Overflows Impacting on Shellfish Waters in England

Storm overflow (SO) discharges to shellfish growing waters are a concern for shellfish growers and may pose a health risk to consumers. We investigated the performance of permitted SOs with a shellfish water spill monitoring requirement against the design criterion of 10 spills per year (averaged over 10 years) used in England. Performance against this criterion over the period 2019–2021 differed between the five water companies whose data was analysed. Across all companies, over half of SOs spilled more frequently than this criterion (percentage greater than 10 spills: 52%, 55% and 57% for 2019, 2020 and 2021, respectively). The number of SOs with the monitoring requirement also differed considerably between the water companies and consequently some companies deal with a significantly higher ‘burden’ than others. The number of SOs spilling more than 100 times in a year also differed between water companies, from 0% (Wessex Water) to 16% (United Utilities). Discharges from SOs can lead to short-term reductions in water quality that may be missed by routine monitoring programmes using faecal indicator bacteria such as E. coli. Such discharges can lead to a higher incidence of norovirus in shellfish and thus potential illness in consumers. We conclude that site-specific impact assessments, supported by spill event-based monitoring, are required given the increased demands on sewerage networks from urban growth and climate change and the need to improve shellfish production area classifications.

Standardization of Epidemiological Surveillance of Rheumatic Heart Disease.

Rheumatic heart disease (RHD) is a long-term sequela of acute rheumatic fever (ARF), which classically begins after an untreated or undertreated infection caused by Streptococcus pyogenes (Strep A). RHD develops after the heart valves are permanently damaged due to ARF. RHD remains a leading cause of morbidity and mortality in young adults in resource-limited and low- and middle-income countries. This article presents case definitions for latent, suspected, and clinical RHD for persons with and without a history of ARF, and details case classifications, including differentiating between definite or borderline according to the 2012 World Heart Federation echocardiographic diagnostic criteria. This article also covers considerations specific to RHD surveillance methodology, including discussions on echocardiographic screening, where and how to conduct active or passive surveillance (eg, early childhood centers/schools, households, primary healthcare), participant eligibility, and the surveillance population. Additional considerations for RHD surveillance, including implications for secondary prophylaxis and follow-up, RHD registers, community engagement, and the negative impact of surveillance, are addressed. Finally, the core elements of case report forms for RHD, monitoring and audit requirements, quality control and assurance, and the ethics of conducting surveillance are discussed.

Standardization of Epidemiological Surveillance of Acute Rheumatic Fever.

Acute rheumatic fever (ARF) is a multiorgan inflammatory disorder that results from the body’s autoimmune response to pharyngitis or a skin infection caused by Streptococcus pyogenes (Strep A). Acute rheumatic fever mainly affects those in low- and middle-income nations, as well as in indigenous populations in wealthy nations, where initial Strep A infections may go undetected. A single episode of ARF puts a person at increased risk of developing long-term cardiac damage known as rheumatic heart disease. We present case definitions for both definite and possible ARF, including initial and recurrent episodes, according to the 2015 Jones Criteria, and we discuss current tests available to aid in the diagnosis.We outline the considerations specific to ARF surveillance methodology, including discussion on where and how to conduct active or passive surveillance (eg, early childhood centers/schools, households, primary healthcare, administrative database review), participant eligibility, and the surveillance population. Additional considerations for ARF surveillance, including implications for secondary prophylaxis and follow-up, ARF registers, community engagement, and the impact of surveillance, are addressed. Finally, the core elements of case report forms for ARF, monitoring and audit requirements, quality control and assurance, and the ethics of conducting surveillance are discussed.

The status and development proposal of carbon sources and sinks monitoring satellite system

In order to mitigate global warming, the international communities actively explore low-carbon and green development methods. According to the Paris Agreement that came into effect in 2016, there will be a global stocktaking plan to carry out every 5 years from 2023 onwards. In September 2020, China proposed a "double carbon" target of carbon peaking before 2030 and carbon neutrality before 2060. Achieving carbon peaking and carbon neutrality goals requires accurate carbon emissions and carbon absorptions. China's existing carbon monitoring methods have insufficient detection accuracy, low spatial resolution, and narrow swath, which are difficult to meet the monitoring requirement of carbon sources and sinks monitoring. In order to meet the needs of carbon stocktaking and support the monitoring and supervision of carbon sources and sinks, it is recommended to make full use of the foundation of the existing satellites, improve the detection technical specifications of carbon sources and sinks monitoring measures, and build a multi-means and comprehensive, LEO-GEO orbit carbon monitoring satellite system to achieve higher precision, higher resolution and multi-dimensional carbon monitoring. On this basis, it is recommended to strengthen international cooperation, improve data sharing policy, actively participate in the development of carbon retrieval algorithm and the setting of international carbon monitoring standards, establish an independent and controllable global carbon monitoring and evaluation system, and contribute China's strength to the global realization of carbon peaking and carbon neutrality.

Forest Fire Spread Monitoring and Vegetation Dynamics Detection Based on Multi-Source Remote Sensing Images

With the increasingly severe damage wreaked by forest fires, their scientific and effective prevention and control has attracted the attention of countries worldwide. The breakthrough of remote sensing technologies implemented in the monitoring of fire spread and early warning has become the development direction for their prevention and control. However, a single remote sensing data collection point cannot simultaneously meet the temporal and spatial resolution requirements of fire spread monitoring. This can significantly affect the efficiency and timeliness of fire spread monitoring. This article focuses on the mountain fires that occurred in Muli County, on 28 March 2020, and in Jingjiu Township on 30 March 2020, in Liangshan Prefecture, Sichuan Province, as its research objects. Multi-source satellite remote sensing image data from Planet, Sentinel-2, MODIS, GF-1, GF-4, and Landsat-8 were used for fire monitoring. The spread of the fire time series was effectively and quickly obtained using the remote sensing data at various times. Fireline information and fire severity were extracted based on the calculated differenced normalized burn ratio (dNBR). This study collected the meteorological, terrain, combustibles, and human factors related to the fire. The random forest algorithm analyzed the collected data and identified the main factors, with their order of importance, that affected the spread of the two selected forest fires in Sichuan Province. Finally, the vegetation coverage before and after the fire was calculated, and the relationship between the vegetation coverage and the fire severity was analyzed. The results showed that the multi-source satellite remote sensing images can be utilized and implemented for time-evolving forest fires, enabling forest managers and firefighting agencies to plan improved firefighting actions in a timely manner and increase the effectiveness of firefighting strategies. For the forest fires in Sichuan Province studied here, the meteorological factors had the most significant impact on their spread compared with other forest fire factors. Among all variables, relative humidity was the most crucial factor affecting the spread of forest fires. The linear regression results showed that the vegetation coverage and dNBR were significantly correlated before and after the fire. The vegetation coverage recovery effects were different in the fire burned areas depending on fire severity. High vegetation recovery was associated with low-intensity burned areas. By combining the remote sensing data obtained by multi-source remote sensing satellites, accurate and macro dynamic monitoring and quantitative analysis of wildfires can be carried out. The study’s results provide effective information on the fires in Sichuan Province and can be used as a technical reference for fire spread monitoring and analysis through remote sensing, enabling accelerated emergency responses.